We are going to analyze an invasive ductal carcinoma breast tissue section from 10x Genomicsis Visium Gene Expression platform.
For the analysis we will mainly use the R package Seurat.
You can download the data from this website or using curl, as shown below. We do not need to do it because it is already in the data folder.
# download files
curl -O https://cf.10xgenomics.com/samples/spatial-exp/1.1.0/V1_Breast_Cancer_Block_A_Section_1/V1_Breast_Cancer_Block_A_Section_1_filtered_feature_bc_matrix.h5
curl -O https://cf.10xgenomics.com/samples/spatial-exp/1.1.0/V1_Breast_Cancer_Block_A_Section_1/V1_Breast_Cancer_Block_A_Section_1_spatial.tar.gz
These are the libraries we are going to use:
library(tidyverse)
library(Seurat)
library(patchwork)
# set seed for reproducibility purposes
set.seed(1243)
# create palette for the cell types from the pals package
cell_type_palette <- c(
"#5A5156", "#E4E1E3", "#F6222E", "#FE00FA",
"#16FF32", "#3283FE", "#FEAF16", "#B00068",
"#1CFFCE", "#90AD1C", "#2ED9FF", "#DEA0FD",
"#AA0DFE", "#F8A19F", "#325A9B", "#C4451C",
"#1C8356", "#85660D", "#B10DA1", "#FBE426",
"#1CBE4F", "#FA0087", "#FC1CBF", "#F7E1A0",
"#C075A6", "#782AB6", "#AAF400", "#BDCDFF",
"#822E1C", "#B5EFB5", "#7ED7D1", "#1C7F93",
"#D85FF7", "#683B79", "#66B0FF", "#3B00FB")
We start by loading the data Visium data. Note that here we start by already loading the filtered expression data. This means that we are only keeping those spots that overlap with the tissue as determined by spaceranger.
If we expect there to be overpermabilization of the tissue or want to get a more general look we can load the raw HDF5 file instead. The raw file contains the matrix for all 5.000 spots comprising the capture area.
sp_obj <- Load10X_Spatial(
data.dir = "data", # Directory where these data is stored
filename = "filtered_feature_bc_matrix.h5", # Name of H5 file containing the feature barcode matrix
assay = "Spatial", # Name of assay
slice = "slice1", # Name of the image
filter.matrix = TRUE # Only keep spots that have been determined to be over tissue
)
The visium data from 10x consists is stored in a Seurat object. This object has a very similar structure to the scRNAseq object:
sp_obj[["Spatial"]][1:5, 1:5]
## 5 x 5 sparse Matrix of class "dgCMatrix"
## AAACAAGTATCTCCCA-1 AAACACCAATAACTGC-1 AAACAGAGCGACTCCT-1
## MIR1302-2HG . . .
## FAM138A . . .
## OR4F5 . . .
## AL627309.1 . . .
## AL627309.3 . . .
## AAACAGGGTCTATATT-1 AAACAGTGTTCCTGGG-1
## MIR1302-2HG . .
## FAM138A . .
## OR4F5 . .
## AL627309.1 . .
## AL627309.3 . .
It adds one slot which contains the images of the Visium experiments as seen below:
sp_obj@images
## $slice1
## Spatial data from the VisiumV1 technology for 3798 samples
## Associated assay: Spatial
## Image key: slice1_
We can visualize the image as follows (we will also store this for later):
(img <- SpatialPlot(
sp_obj, # Name of the Seurat Object
pt.size = 0, # Point size to see spots on the tissue
crop = FALSE # Wether to crop to see only tissue section
) +
NoLegend()
)
sp_obj@images$slice1@scale.factors
## $spot
## [1] 0.08250825
##
## $fiducial
## [1] 286.7012
##
## $hires
## [1] 0.08250825
##
## $lowres
## [1] 0.02475247
##
## attr(,"class")
## [1] "scalefactors"
The goal of this step is to remove poor quality spots and lowly captured genes. To do so we will go over some basic QC steps. Furthermore, due to the nature of the assay during library preparation there can be some lateral diffusion of transcripts. If there are spots not overlapping with the tissue we also need to remove them since these are artifacts of the experiment.
As with single-cell objects, we have some important features that we can use to filter out bad quality spots.
umi_vln_plt <- VlnPlot(
sp_obj,
features = "nCount_Spatial",
pt.size = 0.1) +
NoLegend()
umi_sp_plt <- SpatialFeaturePlot(
sp_obj,
features = "nCount_Spatial")
umi_vln_plt | umi_sp_plt | img
The variability in the distribution of UMIs is related to the tissue architecture, i.e. tumoral regions have a higher cell density than fibrotic regions and thus overlapping spots contain higher counts.
feat_vln_plt <- VlnPlot(
sp_obj,
features = "nFeature_Spatial",
pt.size = 0.1) +
NoLegend()
feat_sp_plt <- SpatialFeaturePlot(
sp_obj,
features = "nFeature_Spatial")
feat_vln_plt | feat_sp_plt | img
Again, here we can see how the number of genes per spot correlates with the structure of the tissue.
We have to compute this two values by calculating the percentage of reads per spot that belong to mitochondrial/ribosomal genes.
# Mitochondrial content
sp_obj[["mt.content"]] <- PercentageFeatureSet(
object = sp_obj,
pattern = "^MT-")
summary(sp_obj[["mt.content"]])
## mt.content
## Min. : 1.118
## 1st Qu.: 2.761
## Median : 3.504
## Mean : 4.009
## 3rd Qu.: 4.678
## Max. :14.415
# Ral contentibosomal
sp_obj[["rb.content"]] <- PercentageFeatureSet(
object = sp_obj,
pattern = "^RPL|^RPS")
summary(sp_obj[["rb.content"]])
## rb.content
## Min. : 6.697
## 1st Qu.:10.765
## Median :11.752
## Mean :11.958
## 3rd Qu.:12.923
## Max. :21.482
SpatialFeaturePlot(
sp_obj,
features = c("mt.content", "rb.content"))
In the case of spatial data, high mitochondrial content is not necessarily an indicator of bad quality spots. Therefore, on its own it is not sufficient to determine which spots to filter out. In this case, they are not pointing towards low quality regions but seem to be reflecting the biological structure of the tissue.
Before filtering:
sp_obj
## An object of class Seurat
## 36601 features across 3798 samples within 1 assay
## Active assay: Spatial (36601 features, 0 variable features)
We are going to filter out genes that have no expression in the tissue.
table(rowSums(as.matrix(sp_obj@assays$Spatial@counts)) == 0)
##
## FALSE TRUE
## 24923 11678
keep_genes <- rowSums(as.matrix(sp_obj@assays$Spatial@counts)) != 0
sp_obj <- sp_obj[keep_genes, ]
We see how we remove 11.678 genes while keeping 24.923.
Furthermore, we set a threshold to filter out spots with very low number of counts (< 500) before proceeding with the downstream analysis. As we can see there are no spots with <500 UMIs so we will not remove any of them.
table(colSums(as.matrix(sp_obj@assays$Spatial@counts)) < 500)
##
## FALSE
## 3798
sp_obj <- subset(
sp_obj,
subset = nCount_Spatial > 500)
After filtering:
sp_obj
## An object of class Seurat
## 24923 features across 3798 samples within 1 assay
## Active assay: Spatial (24923 features, 0 variable features)
Similar to single-cell datasets, preprocessing for spatial data requires normalization, identification of variable features and scaling the counts. To carry out these steps we will use SCTransform which takes into account that different spot complexities observed. Spots overlapping more cell-dense regions will have more UMIs. If we use standard Log Normalization we are removing this biological signal from the dataset.
sp_obj <- SCTransform(
sp_obj,
assay = "Spatial", # assay to pull the count data from
ncells = ncol(sp_obj), # Number of subsampling Spots used to build NB regression, in this case use all
variable.features.n = 3000, # variable features to use after ranking by residual variance
verbose = FALSE
)
Prior to clustering, we perform dimensionality reduction via PCA. We then look at the elbow plot to assess the right number of principal components (PC) to use for downstream analysis.
sp_obj <- RunPCA(
sp_obj,
npcs = 50
)
ElbowPlot(
sp_obj,
ndims = 50
)
We see an elbow at 15 PC, after that the standard deviations are pretty much flat indicating that they aren’t contributing much information. To reduce computational resources and noise we proceed with the first 15 PCs and add another 10 for a total of 25 PCs to make sure we are not loosing biological signal while reducing the noise.
sp_obj <- RunUMAP(
sp_obj,
reduction = "pca",
dims = 1:25
)
Next we compute the K nearest neighbors and find an optimal number of clusters using shared nearest neighbor Louvain modularity based clustering.
sp_obj <- FindNeighbors(
sp_obj,
reduction = "pca",
dims = 1:20
)
sp_obj <- FindClusters(
sp_obj,
resolution = 0.3
)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 3798
## Number of edges: 124079
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9250
## Number of communities: 9
## Elapsed time: 0 seconds
Look at the clustering in the UMAP space and on the tissue:
umap_plt <- DimPlot(
sp_obj,
label = TRUE
)
sp_plt <- SpatialDimPlot(
sp_obj
) +
NoLegend()
umap_plt + sp_plt
ESR1 and ERBB2 (HER2) are the two of the most common mutations in breast cancer, so one way of annotating the tissue is by looking at ESR1 and ERBB2 positive/negative regions.
SpatialFeaturePlot(
sp_obj,
features = c("ESR1", "ERBB2"),
alpha = c(0.1, 1)
) +
(SpatialDimPlot(
sp_obj,
label = TRUE
) +
NoLegend())
From the expression of the two genes above we can do a high-level annotation.
sp_obj@meta.data <- sp_obj@meta.data %>%
mutate(annotation = case_when(
SCT_snn_res.0.3 == 0 ~ "Fibrotic",
SCT_snn_res.0.3 == 1 ~ "Fibrotic",
SCT_snn_res.0.3 == 2 ~ "HER2-/ESR1-",
SCT_snn_res.0.3 == 3 ~ "HER2-/ESR1+",
SCT_snn_res.0.3 == 4 ~ "HER2+/ESR1+",
SCT_snn_res.0.3 == 5 ~ "HER2+/ESR1-",
SCT_snn_res.0.3 == 6 ~ "HER2-/ESR1-",
SCT_snn_res.0.3 == 7 ~ "HER2-/ESR1-",
SCT_snn_res.0.3 == 8 ~ "HER2-/ESR1-")
)
Look at the annotation
# We will define a palette (this is optional)
annot_pal <- c("#E41A1C", "#FF7F00", "#984EA3", "#4DAF4A", "#377EB8")
names(annot_pal) <- c("Fibrotic", "HER2-/ESR1-", "HER2-/ESR1+", "HER2+/ESR1-", "HER2+/ESR1+")
SpatialDimPlot(
sp_obj,
group.by = "annotation",
cols = annot_pal
)
We are going to use SPOTlight in conjunction with a subset of the Tumor Immune Cell Atlas to deconvolute our spots and map immune populations to our tumor section.
# load library
library(SPOTlight)
library(NMF)
Load downsampled version of the Tumor Immune Cell Atlas and explore the metadata we have. We are going to use annotation level 1 for the deconvolution (lv1_annot). Moreover, since the origin of these cells is from different organs and papers we want to minize batch effect. To do so we will only select cells coming from one cancer type which has enough cells for all cell types.
atlas <- readRDS("R_obj/TICAtlas_downsample.rds")
head(atlas@meta.data)
## patient nCount_RNA nFeature_RNA percent.mt gender subtype
## MEL2_2_W461993 MEL2_2 1007 523 10.317460 unknown CM
## MEL2_2_W462036 MEL2_2 1790 797 11.607143 unknown CM
## MEL2_2_W462040 MEL2_2 905 498 13.480663 unknown CM
## MEL2_2_W462050 MEL2_2 1113 669 7.726864 unknown CM
## MEL2_2_W462065 MEL2_2 750 399 10.933333 unknown CM
## MEL2_2_W462066 MEL2_2 815 436 7.239264 unknown CM
## source lv1_annot lv2_annot
## MEL2_2_W461993 melanoma2 T cells naive CD4 memory stem cells
## MEL2_2_W462036 melanoma2 T cells naive CD4 memory stem cells
## MEL2_2_W462040 melanoma2 CD8 cytotoxic CD8 cytotoxic
## MEL2_2_W462050 melanoma2 T helper cells CD4 resident effector memory
## MEL2_2_W462065 melanoma2 CD4 effector memory CD8 cytotoxic
## MEL2_2_W462066 melanoma2 CD8 effector memory CD8 cytotoxic
table(atlas@meta.data$lv1_annot)
##
## B cells CD4 effector memory CD4 naive-memory
## 100 100 100
## CD4 recently activated CD4 transitional memory CD8 cytotoxic
## 100 100 100
## CD8 effector memory CD8 pre-exhausted CD8 terminally exhausted
## 100 100 100
## cDC Macrophages SPP1 mDC
## 100 100 100
## Monocytes NK pDC
## 100 100 100
## Plasma B cells Proliferative T cells naive
## 100 100 100
## T cells regulatory T helper cells TAMs C1QC
## 100 100 100
## TAMs proinflamatory
## 100
First of all we need to compute the markers for the cell types using Seurat’s function FindAllMarkers.
Idents(atlas) <- "lv1_annot"
sc_markers <- FindAllMarkers(
object = atlas,
assay = "RNA",
slot = "data",
only.pos = TRUE
)
saveRDS(sc_markers, "R_obj/filtered_atlas_markers.rds")
sc_markers <- readRDS("R_obj/filtered_atlas_markers.rds")
sc_markers %>%
group_by(cluster) %>%
top_n(5, wt = avg_log2FC) %>%
DT::datatable()
Run the deconvolution using the scRNAseq atlas and the spatial transcriptomics data.
decon_mtrx_ls <- spotlight_deconvolution(
se_sc = atlas, # Single-cell dataset
counts_spatial = sp_obj@assays$Spatial@counts,
clust_vr = "lv1_annot", # Label to use for the deconvolution (cell types)
cluster_markers = sc_markers, # Cell type markers
hvg = 3000, # Number of HVGs to use on top of the markers
min_cont = 0, # minimum expected contribution per cell type and spot
assay = "RNA",
slot = "counts"
)
saveRDS(decon_mtrx_ls, "R_obj/deconvolution_ls.rds")
decon_mtrx_ls <- readRDS("R_obj/deconvolution_ls.rds")
Before even looking at the decomposed spots we can gain insight on how well the model performed by looking at the topic profiles for the cell types.
nmf_mod <- decon_mtrx_ls[[1]]
decon_mtrx <- decon_mtrx_ls[[2]]
rownames(decon_mtrx) <- colnames(sp_obj)
# info on the NMF model
nmf_mod[[1]]
## <Object of class: NMFfit>
## # Model:
## <Object of class:NMFns>
## features: 3480
## basis/rank: 22
## samples: 2200
## theta: 0.5
## # Details:
## algorithm: nsNMF
## seed: NMF
## RNG: 10403L, 624L, ..., -689249108L [e0d3d05f1b721ce7d02aa5d9b936a60e]
## distance metric: 'KL'
## residuals: 3563528
## Iterations: 2000
## Timing:
## user system elapsed
## 1993.28 7.72 2002.77
# deconvolution matrix
head(decon_mtrx)
## B.cells CD4.effector.memory CD4.naive.memory
## AAACAAGTATCTCCCA-1 0.010137316 2.337821e-17 0.08600150
## AAACACCAATAACTGC-1 0.011896373 2.733968e-02 0.11147947
## AAACAGAGCGACTCCT-1 0.038056501 4.511822e-17 0.19188749
## AAACAGGGTCTATATT-1 0.059475727 5.644065e-02 0.20056665
## AAACAGTGTTCCTGGG-1 0.008844078 5.507314e-02 0.05713598
## AAACATTTCCCGGATT-1 0.015016215 5.809065e-02 0.01998702
## CD4.recently.activated CD4.transitional.memory CD8.cytotoxic
## AAACAAGTATCTCCCA-1 0.02754046 0.06505139 0.02538960
## AAACACCAATAACTGC-1 0.17362920 0.05881900 0.05766773
## AAACAGAGCGACTCCT-1 0.19681667 0.15305800 0.00000000
## AAACAGGGTCTATATT-1 0.11554120 0.04922768 0.14375044
## AAACAGTGTTCCTGGG-1 0.15684167 0.03793254 0.08376386
## AAACATTTCCCGGATT-1 0.20397762 0.05225016 0.04142339
## CD8.effector.memory CD8.pre.exhausted
## AAACAAGTATCTCCCA-1 0.08938036 0.000000000
## AAACACCAATAACTGC-1 0.15701319 0.015386639
## AAACAGAGCGACTCCT-1 0.04832925 0.059781263
## AAACAGGGTCTATATT-1 0.05641382 0.012147690
## AAACAGTGTTCCTGGG-1 0.10444017 0.003339111
## AAACATTTCCCGGATT-1 0.17134106 0.015946950
## CD8.terminally.exhausted cDC Macrophages.SPP1
## AAACAAGTATCTCCCA-1 0.07805629 0.010078568 0.4082143
## AAACACCAATAACTGC-1 0.03765994 0.007942235 0.1799766
## AAACAGAGCGACTCCT-1 0.06779712 0.024202138 0.0000000
## AAACAGGGTCTATATT-1 0.08356837 0.029321452 0.0000000
## AAACAGTGTTCCTGGG-1 0.03099807 0.012654295 0.2463184
## AAACATTTCCCGGATT-1 0.03965314 0.007187891 0.2171855
## mDC Monocytes NK pDC
## AAACAAGTATCTCCCA-1 0.002396415 0.021046132 0.03655861 0.008410512
## AAACACCAATAACTGC-1 0.006540093 0.014949579 0.01181640 0.009304769
## AAACAGAGCGACTCCT-1 0.006628213 0.040366698 0.02113192 0.015695482
## AAACAGGGTCTATATT-1 0.004340445 0.020529591 0.04323099 0.016729485
## AAACAGTGTTCCTGGG-1 0.006509693 0.005230682 0.01830138 0.009425111
## AAACATTTCCCGGATT-1 0.006827030 0.011141659 0.01349643 0.007538095
## Plasma.B.cells Proliferative T.cells.naive
## AAACAAGTATCTCCCA-1 0.01250217 0.01935377 0.006317957
## AAACACCAATAACTGC-1 0.01391179 0.03434166 0.010680207
## AAACAGAGCGACTCCT-1 0.02152306 0.00000000 0.002864956
## AAACAGGGTCTATATT-1 0.01215923 0.00000000 0.026097732
## AAACAGTGTTCCTGGG-1 0.01330315 0.04682918 0.017745900
## AAACATTTCCCGGATT-1 0.01436761 0.04330740 0.014661432
## T.cells.regulatory T.helper.cells TAMs.C1QC
## AAACAAGTATCTCCCA-1 0.06032369 0.01155677 0.00000000
## AAACACCAATAACTGC-1 0.02565893 0.02467962 0.00000000
## AAACAGAGCGACTCCT-1 0.04413469 0.02156776 0.03151644
## AAACAGGGTCTATATT-1 0.02927158 0.01506484 0.01376969
## AAACAGTGTTCCTGGG-1 0.03612121 0.02281536 0.00000000
## AAACATTTCCCGGATT-1 0.02360271 0.01624148 0.00000000
## TAMs.proinflamatory res_ss
## AAACAAGTATCTCCCA-1 0.021684185 5.217910e-05
## AAACACCAATAACTGC-1 0.009306901 5.202812e-02
## AAACAGAGCGACTCCT-1 0.014642352 7.086854e-33
## AAACAGGGTCTATATT-1 0.012352748 1.487515e-32
## AAACAGTGTTCCTGGG-1 0.026376988 7.714221e-02
## AAACATTTCCCGGATT-1 0.006756593 4.328916e-02
Look at how specific the topic profiles are for each cell type. Ideally we want to see how each cell type has its own unique topic profile. This means the model has learnt a unique gene signature for that cell type.
h <- NMF::coef(nmf_mod[[1]])
rownames(h) <- paste("Topic", 1:nrow(h), sep = "_")
topic_profile_plts <- SPOTlight::dot_plot_profiles_fun(
h = h,
train_cell_clust = nmf_mod[[2]])
topic_profile_plts[[2]]
We also want to take a look at the topic profiles of the individual cells. We want to see how all the cells from the same cell type share the same topic profiles to make sure the learned signature is robust. In this plot each facet shows all the cells from the same cell type.
topic_profile_plts[[1]] +
theme(axis.text.x = element_blank())
Lastly we can take a look at which genes the model learned for each topic. Higher values indicate that the gene is more relevant for that topic. In the below table we can see how the top genes for topic 1 are characteristic for B cells (i.e. CD79A, CD79B, MS4A1, IGHD…).
sign <- basis(nmf_mod[[1]])
colnames(sign) <- paste0("Topic", seq_len(ncol(sign)))
# This can be dynamically visualized with DT as shown below
DT::datatable(sign, filter = "top")
Let’s now visualize how the deconvoluted spots on the the Seurat object.
# We will only add the deconvolution matrix
decon_mtrx <- decon_mtrx_ls[[2]]
decon_mtrx <- decon_mtrx[, colnames(decon_mtrx) != "res_ss"]
decon_mtrx <- decon_mtrx[, !is.na(colnames(decon_mtrx))]
# Set as 0 those cell types that contribute <3% to the spot
decon_mtrx[decon_mtrx < 0.02] <- 0
# Add deconvolution results to Seurat object
sp_obj@meta.data <- cbind(sp_obj@meta.data, decon_mtrx)
saveRDS(sp_obj, "R_obj/breast_slide_deconvoluted.rds")
We have an object with the deconvolution information already added:
sp_obj <- readRDS("R_obj/breast_slide_deconvoluted.rds")
The first thing we can do is look at the spatial scatterpie. This plot represents each spot as an individual piechart where the proportion of each cell type within that spot is represented.
ct <- colnames(decon_mtrx)
scatterpie_plot(
se_obj = sp_obj,
cell_types_all = ct,
pie_scale = 0.3) +
coord_fixed(ratio = 1) +
scale_fill_manual(values = cell_type_palette)
## [1] "Using slice slice1"
As we can see when we look at all the cell types at the same time we are not able to discern clear patterns. To improve the visualization we will remove those cell types that are found in >80% of the spot and keep those that aren’t ubiquitouslyy expressed.
# keep only cell types that are present in less than 80% of the spots
keep_0.8 <- colSums(sp_obj@meta.data[, ct] > 0) < 0.8 * ncol(sp_obj)
# but not those that were not found on the tissue
keep_g0 <- colSums(sp_obj@meta.data[, ct] > 0) > 0
# select cell types fullfiling the conditions
ct_var <- colnames(sp_obj@meta.data[, ct])[keep_0.8 & keep_g0]
ct_var
## [1] "B.cells" "CD8.pre.exhausted" "cDC"
## [4] "mDC" "Monocytes" "NK"
## [7] "pDC" "Plasma.B.cells" "T.cells.naive"
## [10] "T.cells.regulatory" "T.helper.cells" "TAMs.C1QC"
## [13] "TAMs.proinflamatory"
Plot the spatial scatterpie only with the cell types of interest
scatterpie_plot(
se_obj = sp_obj,
cell_types_all = ct_var,
pie_scale = 0.3) +
coord_fixed(ratio = 1) +
scale_fill_manual(values = cell_type_palette)
## [1] "Using slice slice1"
Next, we can visualize the individual cell type proportions on the spatial slide.
scaleFUN <- function(x) sprintf("%.2f", x)
SpatialPlot(
object = sp_obj,
features = ct,
alpha = c(0, 1),
ncol = 4, image.alpha = 0) &
scale_fill_gradientn(
colors = grDevices::heat.colors(10, rev = TRUE),
# 2 decimals in the legend
labels = scaleFUN,
n.breaks = 4)
Lets now see the frequencies of selected cell types across the regions in the tissue. We will focus on pre-exhausted T cells and macrophages as they can give an idea of how infiltrated the tumor can be.
VlnPlot(
sp_obj,
features = c("Monocytes", "TAMs.C1QC", "TAMs.proinflamatory", "CD8.terminally.exhausted", "CD8.pre.exhausted"),
group.by = "annotation",
cols = annot_pal,
pt.size = 0.2
)
plt_annot <- SpatialDimPlot(
sp_obj,
group.by = "annotation",
cols = annot_pal
)
img | plt_annot
We can see that in the HER2+/ESR1- (dark red) region we have an abundance of pre-exhausted CD8 T cells as well as pro-inflamatory macrophages. The presence of both subtypes at the same time in a tumor region, can be indicative of the presence of a highly infiltrated tumor, often referred to as a “hot” tumor.
This type of tumors often have a better response to treatment, opposite to cold regions, where little to no immune cells are able to infiltrate the tumor, conforming a immune excluded section, often linked to worse treatment response.
sessionInfo()
## R version 4.1.0 (2021-05-18)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 18363)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Spain.1252 LC_CTYPE=Spanish_Spain.1252
## [3] LC_MONETARY=Spanish_Spain.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Spain.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] imager_0.42.11 magrittr_2.0.1 cowplot_1.1.1
## [4] NMF_0.23.0 Biobase_2.53.0 BiocGenerics_0.40.0
## [7] cluster_2.1.2 rngtools_1.5.2 pkgmaker_0.32.2
## [10] registry_0.5-1 SPOTlight_0.1.7 patchwork_1.1.1
## [13] SeuratObject_4.0.4 Seurat_4.0.6 forcats_0.5.1
## [16] stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
## [19] readr_2.1.1 tidyr_1.1.4 tibble_3.1.6
## [22] ggplot2_3.3.5 tidyverse_1.3.1
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.4.1 plyr_1.8.6
## [4] igraph_1.2.10 lazyeval_0.2.2 splines_4.1.0
## [7] crosstalk_1.2.0 listenv_0.8.0 scattermore_0.7
## [10] gridBase_0.4-7 digest_0.6.29 foreach_1.5.1
## [13] htmltools_0.5.2 tiff_0.1-10 fansi_0.5.0
## [16] doParallel_1.0.16 tensor_1.5 ROCR_1.0-11
## [19] tzdb_0.2.0 globals_0.14.0 modelr_0.1.8
## [22] matrixStats_0.61.0 spatstat.sparse_2.1-0 jpeg_0.1-9
## [25] colorspace_2.0-2 rvest_1.0.2 ggrepel_0.9.1
## [28] haven_2.4.3 xfun_0.29 crayon_1.4.2
## [31] jsonlite_1.7.2 scatterpie_0.1.7 spatstat.data_2.1-2
## [34] iterators_1.0.13 survival_3.2-13 zoo_1.8-9
## [37] glue_1.6.0 polyclip_1.10-0 gtable_0.3.0
## [40] leiden_0.3.9 future.apply_1.8.1 abind_1.4-5
## [43] scales_1.1.1 DBI_1.1.2 miniUI_0.1.1.1
## [46] Rcpp_1.0.7 viridisLite_0.4.0 xtable_1.8-4
## [49] reticulate_1.22 spatstat.core_2.3-2 bit_4.0.4
## [52] DT_0.20 htmlwidgets_1.5.4 httr_1.4.2
## [55] RColorBrewer_1.1-2 ellipsis_0.3.2 ica_1.0-2
## [58] pkgconfig_2.0.3 farver_2.1.0 sass_0.4.0
## [61] uwot_0.1.11 dbplyr_2.1.1 deldir_1.0-6
## [64] utf8_1.2.2 tidyselect_1.1.1 labeling_0.4.2
## [67] rlang_0.4.12 reshape2_1.4.4 later_1.3.0
## [70] munsell_0.5.0 cellranger_1.1.0 tools_4.1.0
## [73] cli_3.1.0 generics_0.1.1 broom_0.7.10
## [76] ggridges_0.5.3 evaluate_0.14 fastmap_1.1.0
## [79] yaml_2.2.1 goftest_1.2-3 knitr_1.37
## [82] bit64_4.0.5 fs_1.5.2 fitdistrplus_1.1-6
## [85] RANN_2.6.1 readbitmap_0.1.5 pbapply_1.5-0
## [88] future_1.23.0 nlme_3.1-153 mime_0.12
## [91] ggrastr_1.0.1 xml2_1.3.3 hdf5r_1.3.5
## [94] compiler_4.1.0 rstudioapi_0.13 beeswarm_0.4.0
## [97] plotly_4.10.0 png_0.1-7 spatstat.utils_2.3-0
## [100] reprex_2.0.1 tweenr_1.0.2 bslib_0.3.1
## [103] stringi_1.7.6 highr_0.9 RSpectra_0.16-0
## [106] lattice_0.20-45 Matrix_1.4-0 vctrs_0.3.8
## [109] pillar_1.6.4 lifecycle_1.0.1 spatstat.geom_2.3-1
## [112] lmtest_0.9-39 jquerylib_0.1.4 RcppAnnoy_0.0.19
## [115] data.table_1.14.2 irlba_2.3.5 httpuv_1.6.4
## [118] R6_2.5.1 promises_1.2.0.1 bmp_0.3
## [121] KernSmooth_2.23-20 gridExtra_2.3 vipor_0.4.5
## [124] parallelly_1.30.0 codetools_0.2-18 MASS_7.3-54
## [127] assertthat_0.2.1 withr_2.4.3 sctransform_0.3.2
## [130] mgcv_1.8-38 parallel_4.1.0 hms_1.1.1
## [133] ggfun_0.0.4 grid_4.1.0 rpart_4.1-15
## [136] rmarkdown_2.11 Cairo_1.5-12.2 Rtsne_0.15
## [139] ggforce_0.3.3 shiny_1.7.1 lubridate_1.8.0
## [142] ggbeeswarm_0.6.0